Abstract

A novel method of determining reservoir characteristic curves based on high-resolution resource satellite data was proposed in this paper, using remote sensing processing and analysis technology. According to the physical characteristics of absorption, radiation and reflection of surface water on ultraviolet, visible, near-infrared bands, etc., the satellite images at different reservoir water level and different periods were processed to analyze the relationship of measured water level corresponding to the water area. Based on the relationship, the relevance among reservoir water level, water surface area, and reservoir capacity was established, so as to determine the reservoir characteristic curve. The method was applied and validated at Jinshuitan Reservoir and Shitang Reservoir in the Ou River Basin. The results show that this method has high accuracy, and the maximum relative error between calculating values and measured values at different water level are −2.33% and −2.11% in Jinshuitan Reservoir and Shitang Reservoir, respectively. The method improves the convenience of determining the reservoir characteristic curve greatly, and the storage capacity of the reservoir can be calculated rapidly by this method.

Highlights

  • A novel method of determining reservoir characteristic curves based on high-resolution resource satellite data was proposed in this paper, by using remote sensing processing and analysis technology.

  • Based on the methodology put forward in this paper, reservoir characteristic curves can be determined on the digital platform expediently and accurately. The field measurement can be left out.

INTRODUCTION

The reservoir characteristic curves mainly refer to the relationship curves of reservoir water level–reservoir capacity (LC curve) and reservoir water level–reservoir water surface area (LA curve). These curves are the main parameters reflecting the scale and characteristics of the reservoir as well as an important basis for the design of the reservoir dam, the determination of the reservoir operation mode, and the flood control review, detection and reinforcement of the reservoir dam (MWR of PRC 2017). Previously, in the field of hydrology-water resources and hydraulic engineering, the methods for determining the reservoir characteristic curve were mostly reckoned based on the topographic survey data (Zhang et al. 2019). But, in fact, there are many disadvantages in these methods, for example, small field of vision (NASA 1998; Ren et al. 2021), heavy field surveying workload (Pal et al. 2018; Cucchiaro et al. 2019; Galicia et al. 2019), and low accuracy (Bossler et al. 2001; Sun et al. 2021). With the development of remote sensing technology, resource satellite data are extensively used in correlative research of reservoirs and dams. Huang et al. (2009) identified river bed for check-dam engineering using SPOT-5 images. Hui et al. (2010) planned and designed the dam system in a grid-based collaborative virtual geographic environment based on resource satellite data. Meanwhile, the watershed underlying surface information was accurately identified by remote sensing data. Boix-Fayos et al. (2008) assessed the impact of land use change and check dams on catchment sediment yield. Liu (2021) extracted the planting structure of crops in Hetao irrigated area based on Sentinel-2. In the domain of water pollution control, Li et al. (2016) and Wang et al. (2019) assessed the non-point source pollution risks in the water protection zone of the reservoir area based on remote sensing images. Also, Yao et al. (2020) studied water pollution control methods in the Yangtze River Basin by artificial intelligence technology based on satellite data. In addition to these researches, the assessment of erosion in the reservoir area was carried out based on resources satellite images (Abedini et al. 2012; Wang et al. 2016; Arabameri et al. 2019). In this paper, a novel method to determine the reservoir characteristic curve was carried out, which is proposed on resource satellite data processing. By using this methed, the accuracy of the characteristic curves of reservoirs will be improved, and the workload will be reduced. As demonstrations, the characteristic curves of Jinshuitan Reservoir and Shitang Reservoir were determined accurately and efficiently.

STUDY AREA AND RESOURCE SATELLITE DATA

Study area

Jinshuitan Reservoir and Shitang Reservoir are located in Longquan Stream, a Daxi tributary at the upper reaches of the Ou River, the second largest water system in Zhejiang Province, as shown in Figure 1. The catchment area of Jinshuitan Reservoir is 2,773 km2, accounting for 77.8% of the total area of the Longquan Stream Basin, and the catchment area of Shitang Reservoir is 467 km2. The control catchment area of the two reservoirs is 3,240 km2 in total, accounting for 18.1% of the total area of the Ou River Basin. The catchment areas of the two reservoirs are located in the temperate monsoon climate zone with mild climate and abundant rainfall. The average annual precipitation is 1,833.8 mm. The average annual flow of the dam site of Jinshuitan Reservoir is 100 m3/s, and the total average annual runoff is 31.5 × 108m3. The distribution of runoff during the year is extremely uneven. The runoff in the rainy season from April to June accounts for 58.1% of the yearly total, of which the most concentrated is in June, accounting for 24.8% of the yearly total. The runoff in the second flood season from July to October only accounts for 21.64% of the yearly total. The dam site of Shitang Reservoir has an average annual flow of 109 m3/s, and a total average annual runoff of 34.4 × 108m3. There are three hydrology stations, nine precipitation stations, one evaporation station, and three water level stations in the whole area, as shown in Figure 1. The ground-breaking of Jinshuitan Reservoir was made in October 1978. Its main project construction was commenced in October 1981, closure was conducted on October 31, 1983, and impoundment was started on June 24, 1986. The construction of Shitang Reservoir was officially commenced on July 1, 1985, and impoundment was started on December 31, 1988.

Figure 1

Jinshuitan-Shitang Reservoir Basin.

Figure 1

Jinshuitan-Shitang Reservoir Basin.

China–Brazil earth resources satellite

The China–Brazil Earth Resources Satellite is a satellite (code CBERS) approved by the joint protocol of the Chinese and Pakistani governments in 1988 and jointly invested and developed by the two countries (Zhang et al. 2021). On October 14, 1999, China–Brazil Earth Resources Satellite 01 (CBERS-01) was successfully launched, and it operated in orbit for 3 years and 10 months. Satellite 02 (CBERS-02) was launched on October 21, 2003. The China–Brazil Earth Resources Satellite has a flying height of 778 km and an operation cycle of 100.26 min, which means that it can cover the entire Earth within 26 days. The CCD camera carried on the satellite can continuously shoot the ground image in the range of 113 × 113 km with a ground resolution of 19.5 × 19.5 m. It has four bands in visible and near-infrared spectral ranges and one panchromatic band. The China–Brazil Earth Resources Satellite flies over Jinshuitan Reservoir and Shitang Reservoir generally at 9:30–10:30 a.m.

Landsat data set

Since 1972 when the first land satellite was launched, the United States has successively launched seven similar Earth resources satellites (Liu et al. 2021; Mo et al. 2021). Every new satellite has higher data quality and resolution, and a large number of high-resolution multi-spectral remote sensing data has been provided to users around the world. As the computer technology is developed rapidly, the Earth resources satellite data have been widely used in various fields, such as agriculture, forestry, water conservancy, geography, geology, petroleum, and military fields. The Landsat currently flying at a high altitude has a flying height of more than 700 km and an operation cycle of 98.9 min, which means that it can cover the entire Earth within 16 days. The satellite is equipped with a TM and an ETM+. It can continuously shoot the ground image in the range of 185 × 185 km with a ground resolution of 30 × 30 m (15 × 15 m in panchromatic band). The imaging instrument will automatically turn off in the case of rain and a cloud amount of greater than 70%. The time for the Landsat to fly over Jinshuitan Reservoir and Shitang Reservoir is also generally 9:30–10:30 a.m.

Figure 2 shows the satellite color composite image of the area dated July 24, 2008 where Jinshuitan Reservoir and Shitang Reservoir are located and Figure 3 shows the remote sensing composite satellite image imitating natural color of the water catchment of the two reservoirs.

Figure 2

Color composition Landsat image of Jinshuitan Reservoir and Shitang Reservoir.

Figure 2

Color composition Landsat image of Jinshuitan Reservoir and Shitang Reservoir.

Figure 3

Color composition RS image of Jinshuitan Reservoir and Shitang Reservoir Basin.

Figure 3

Color composition RS image of Jinshuitan Reservoir and Shitang Reservoir Basin.

Data preparation

At present, the types of image data that can be provided by satellite ground receiving stations in China include photo data, negative film data, and digital data. Among them, digital data have the highest accuracy. The satellite image in the form of digital data was used in the research.

Since the construction of Jinshuitan Reservoir and Shitang Reservoir, the water storage in the reservoir area has experienced different water levels. The highest water level of Jinshuitan Reservoir was 186.66 m, and that of Shitang Reservoir was 103.04 m. According to the highest and lowest water levels that have occurred in the reservoirs and in comprehensive consideration of the measured synchronous water level data, a total of 36 satellite images were used in the research, 10 from China–Brazil Earth Resources Satellite, and 26 from Landsat. Twenty-five images were used for Jinshuitan Reservoir and its corresponding water table drop was about 164.8–184.5 m and average interval about 0.79 m. Eleven images were used for Shitang Reservoir, and its corresponding water table drop was about 98.0–102.5 m and average interval about 0.41 m. The satellite data used are shown in Tables 1 and 2.

Table 1

Satellite data and synchronous water level data of Jinshuitan Reservoir

No.DateTimeWater level (m)Satellite type
1993.06.26 09:54:39 184.44 Landsat (TM) 
2006.05.29 10:24:29 184.31 Landsat (TM) 
1992.07.09 09:55:16 183.61 Landsat (TM) 
1993.07.12 09:54:36 182.92 Landsat (TM) 
2002.07.13 10:20:35 181.81 Landsat (ETM) 
2008.07.05 10:19:20 181.12 Landsat (TM) 
2008.07.24 10:45:13 180.31 CBERS (CCD) 
2006.09.27 10:28:51 179.60 CBERS (CCD) 
2005.06.16 10:40:15 178.48 CBERS (CCD) 
10 2004.10.30 10:16:59 177.80 Landsat (TM) 
11 2004.07.26 10:14:46 176.93 Landsat (TM) 
12 2004.11.20 10:42:17 176.23 CBERS (CCD) 
13 2000.10.11 10:22:33 175.35 Landsat (ETM) 
14 2006.03.29 10:34:11 174.58 CBERS (CCD) 
15 2006.12.23 10:26:55 173.54 Landsat (TM) 
16 2005.10.17 10:20:14 172.55 Landsat (TM) 
17 2008.02.28 10:22:33 171.57 Landsat (TM) 
18 2006.01.10 10:36:18 170.51 CBERS (CCD) 
19 2004.03.05 10:42:58 169.90 CBERS (CCD) 
20 2003.12.15 10:10:46 169.10 Landsat (TM) 
21 2003.01.13 10:04:30 168.00 Landsat (TM) 
22 2001.03.04 10:22:30 166.96 Landsat (ETM) 
23 2001.01.07 10:11:56 166.13 Landsat (TM) 
24 1997.05.04 10:00:10 165.21 Landsat (TM) 
25 1996.01.10 09:35:06 164.78 Landsat (TM) 
No.DateTimeWater level (m)Satellite type
1993.06.26 09:54:39 184.44 Landsat (TM) 
2006.05.29 10:24:29 184.31 Landsat (TM) 
1992.07.09 09:55:16 183.61 Landsat (TM) 
1993.07.12 09:54:36 182.92 Landsat (TM) 
2002.07.13 10:20:35 181.81 Landsat (ETM) 
2008.07.05 10:19:20 181.12 Landsat (TM) 
2008.07.24 10:45:13 180.31 CBERS (CCD) 
2006.09.27 10:28:51 179.60 CBERS (CCD) 
2005.06.16 10:40:15 178.48 CBERS (CCD) 
10 2004.10.30 10:16:59 177.80 Landsat (TM) 
11 2004.07.26 10:14:46 176.93 Landsat (TM) 
12 2004.11.20 10:42:17 176.23 CBERS (CCD) 
13 2000.10.11 10:22:33 175.35 Landsat (ETM) 
14 2006.03.29 10:34:11 174.58 CBERS (CCD) 
15 2006.12.23 10:26:55 173.54 Landsat (TM) 
16 2005.10.17 10:20:14 172.55 Landsat (TM) 
17 2008.02.28 10:22:33 171.57 Landsat (TM) 
18 2006.01.10 10:36:18 170.51 CBERS (CCD) 
19 2004.03.05 10:42:58 169.90 CBERS (CCD) 
20 2003.12.15 10:10:46 169.10 Landsat (TM) 
21 2003.01.13 10:04:30 168.00 Landsat (TM) 
22 2001.03.04 10:22:30 166.96 Landsat (ETM) 
23 2001.01.07 10:11:56 166.13 Landsat (TM) 
24 1997.05.04 10:00:10 165.21 Landsat (TM) 
25 1996.01.10 09:35:06 164.78 Landsat (TM) 
Table 2

Satellite data and synchronous water level data of Shitang Reservoir

No.DateTimeWater level (m)Satellite type
2008.05.02 10:21:04 102.23 Landsat (TM) 
2008.11.29 10:54:07 102.12 CBERS (CCD) 
2008.02.17 10:52:30 101.84 CBERS (CCD) 
2007.01.08 10:27:02 101.56 Landsat (TM) 
2007.01.09 10:26:18 101.10 CBERS (CCD) 
2001.03.12 10:12:18 100.89 Landsat (TM) 
1991.04.02 09:53:50 100.23 Landsat (TM) 
2003.01.13 10:04:30 99.90 Landsat (TM) 
2000.05.04 10:24:27 99.23 Landsat (ETM) 
10 1991.11.12 09:56:24 98.67 Landsat (TM) 
11 1991.10.27 09:56:20 97.96 Landsat (TM) 
No.DateTimeWater level (m)Satellite type
2008.05.02 10:21:04 102.23 Landsat (TM) 
2008.11.29 10:54:07 102.12 CBERS (CCD) 
2008.02.17 10:52:30 101.84 CBERS (CCD) 
2007.01.08 10:27:02 101.56 Landsat (TM) 
2007.01.09 10:26:18 101.10 CBERS (CCD) 
2001.03.12 10:12:18 100.89 Landsat (TM) 
1991.04.02 09:53:50 100.23 Landsat (TM) 
2003.01.13 10:04:30 99.90 Landsat (TM) 
2000.05.04 10:24:27 99.23 Landsat (ETM) 
10 1991.11.12 09:56:24 98.67 Landsat (TM) 
11 1991.10.27 09:56:20 97.96 Landsat (TM) 

Note: TM is the Landsat TM image data, with a resolution of 30 m; ETM is the Landsat ETM image data, with a resolution of 15 m; and CCD is the CCD image data of the China–Brazil Earth Resources Satellite, with a resolution of 20 m.

REMOTE SENSING METHOD OF DETERMINING RESERVOIR CHARACTERISTIC CURVES

Geometric correction of satellite images

When the resource satellite scans and images a ground object at a high altitude, the scanning image and the actual position are systematically distorted due to factors such as the flying attitude, height and speed of the resource satellite, the Earth rotation, and the uneven scanning speed. Therefore, the resource satellite data obtained from the ground station must be corrected so that the remote sensing data processing results can be consistent with the actual terrain. Although the remote sensing satellite ground station has done routine correction on the data after reception, it is still insufficient for topographic survey and area measurement with a high-accuracy requirement, and geometric precision correction must be performed, that is, geometric position checking is performed using ground control points to correct the remote sensing satellite image onto a unified geodetic coordinate grid.

The method used in the geometric precision correction was the cubic convolution, the base map was the ‘Shanghai-Jiangsu-Zhejiang (Zhejiang Area) 1:50000 Electronic Map’ provided by the First Surveying and Mapping Institute of Zhejiang Province, and a total of 12 points were selected as the correction control points. The cubic convolution was based on the sinC function and adopted the following interpolation function:
formula
(1)
where and are the cutoff frequency of the spatial frequency domain. Usually, it uses a polynomial to approximate the ideal interpolation function. The cubic convolution uses a set of polynomials to approximate the theoretical sinC function at different intervals, namely:
formula
(2)

The interpolation generates a high-accuracy result. It not only maintains the continuity of the pixel without reducing the resolution, but also has the effect of edge enhancement, and the corrected satellite image can be consistent with the topographic map. Both the fine correction processing of the satellite images and the processing of various other images used in the research were completed on the ENVI platform of the remote sensing image processing system.

Satellite image enhancement processing

The ground object spectrum received by the land satellite is comprehensive information of various objects on the ground, and image enhancement techniques are required to highlight the water bodies. The method used was edge enhancement and filtering. This method scans the entire image using a filter kernel to highlight the water and land boundaries. Then, on this basis, the remote sensing platform is used to identify water bodies.

Identification of water bodies on satellite images

In order to obtain accurate reservoir water surface area, water body identification on remote sensing images is an important link. The water radiation rate on the near-infrared band is obviously single and lower than other ground objects, thus the water bodies can be effectively determined. Therefore, a suitable near-infrared band is selected to determine the water body threshold, so as to determine the water bodies on the remote sensing images. For the pixel identification at the land–water interface, the ratio calculation method is used for water body identification. To further enhance the accuracy of area calculation, remote sensing images are converted into images of 15 × 15 m pixels using bicubic method.

Calculation of reservoir water surface area and capacity

After water body identification, the water body pixels on each satellite image are counted one by one, and the water surface area value of the reservoir area at different water levels can be obtained. No human errors will be caused to the area obtained using the method and the accuracy is high. The statistical values of the water surface area corresponding to the satellite image maps of Jinshuitan Reservoir and Shitang Reservoir at different water levels are shown in Tables 3 and 4. The water surface shapes are arranged from large to small as shown in Figures 4 and 5.

Table 3

Analysis of surface water area in Landsat images of Jinshuitan Reservoir

No.DateWater level (m)Pixel numbersWater area (km2)
1993.06.26 184.44 154,994 34.8737 
2006.05.29 184.31 154,239 34.7038 
1992.07.09 183.61 151,030 33.9818 
1993.07.12 182.92 147,686 33.2294 
2002.07.13 181.81 143,456 32.2776 
2008.07.05 181.12 140,245 31.5551 
2008.07.24 180.31 137,872 31.0212 
2006.09.27 179.60 134,613 30.2879 
2005.06.16 178.48 130,190 29.2928 
10 2004.10.30 177.80 126,887 28.5496 
11 2004.07.26 176.93 124,253 27.9569 
12 2004.11.20 176.23 122,195 27.4939 
13 2000.10.11 175.35 119,757 26.9453 
14 2006.03.29 174.58 116,367 26.1826 
15 2006.12.23 173.54 113,288 25.4898 
16 2005.10.17 172.55 110,079 24.7678 
17 2008.02.28 171.57 106,285 23.9141 
18 2006.01.10 170.51 103,953 23.3894 
19 2004.03.05 169.90 102,003 22.9507 
20 2003.12.15 169.10 99,503 22.3882 
21 2003.01.13 168.00 97,037 21.8333 
22 2001.03.04 166.96 93,816 21.1086 
23 2001.01.07 166.13 91,458 20.5781 
24 1997.05.04 165.21 89,297 20.0918 
25 1996.01.10 164.78 88,166 19.8374 
No.DateWater level (m)Pixel numbersWater area (km2)
1993.06.26 184.44 154,994 34.8737 
2006.05.29 184.31 154,239 34.7038 
1992.07.09 183.61 151,030 33.9818 
1993.07.12 182.92 147,686 33.2294 
2002.07.13 181.81 143,456 32.2776 
2008.07.05 181.12 140,245 31.5551 
2008.07.24 180.31 137,872 31.0212 
2006.09.27 179.60 134,613 30.2879 
2005.06.16 178.48 130,190 29.2928 
10 2004.10.30 177.80 126,887 28.5496 
11 2004.07.26 176.93 124,253 27.9569 
12 2004.11.20 176.23 122,195 27.4939 
13 2000.10.11 175.35 119,757 26.9453 
14 2006.03.29 174.58 116,367 26.1826 
15 2006.12.23 173.54 113,288 25.4898 
16 2005.10.17 172.55 110,079 24.7678 
17 2008.02.28 171.57 106,285 23.9141 
18 2006.01.10 170.51 103,953 23.3894 
19 2004.03.05 169.90 102,003 22.9507 
20 2003.12.15 169.10 99,503 22.3882 
21 2003.01.13 168.00 97,037 21.8333 
22 2001.03.04 166.96 93,816 21.1086 
23 2001.01.07 166.13 91,458 20.5781 
24 1997.05.04 165.21 89,297 20.0918 
25 1996.01.10 164.78 88,166 19.8374 
Table 4

Analysis of surface water area in Landsat images of Shitang Reservoir

No.DateWater level (m)Pixel numbersWater area (km2)
2008.05.02 102.23 30,509 6.8645 
2008.11.29 102.12 30,094 6.7712 
2008.02.17 101.84 29,545 6.6476 
2007.01.08 101.56 28,750 6.4688 
2007.01.09 101.10 27,708 6.2343 
2001.03.12 100.89 27,010 6.0773 
1991.04.02 100.23 25,598 5.7596 
2003.01.13 99.90 24,949 5.6135 
2000.05.04 99.23 23,346 5.2527 
10 1991.11.12 98.67 22,106 4.9739 
11 1991.10.27 97.96 20,597 4.6343 
No.DateWater level (m)Pixel numbersWater area (km2)
2008.05.02 102.23 30,509 6.8645 
2008.11.29 102.12 30,094 6.7712 
2008.02.17 101.84 29,545 6.6476 
2007.01.08 101.56 28,750 6.4688 
2007.01.09 101.10 27,708 6.2343 
2001.03.12 100.89 27,010 6.0773 
1991.04.02 100.23 25,598 5.7596 
2003.01.13 99.90 24,949 5.6135 
2000.05.04 99.23 23,346 5.2527 
10 1991.11.12 98.67 22,106 4.9739 
11 1991.10.27 97.96 20,597 4.6343 
Figure 4

Maps of changes in water surface area of Jinshuitan Reservoir.

Figure 4

Maps of changes in water surface area of Jinshuitan Reservoir.

Figure 5

Water surface area of Shitang Reservoir at different water levels.

Figure 5

Water surface area of Shitang Reservoir at different water levels.

After the water surface area at different water levels is analyzed, the reservoir water level–reservoir water surface area curves of Jinshuitan Reservoir and Shitang Reservoir can be configured using the polynomial curve equation configuration software under the platform of satellite remote sensing image processing system, as shown below:
formula
(3)
formula
(4)
where Aj and As are the water surface areas (km2) of Jinshuitan Reservoir and Shitang Reservoir, respectively, and H is the reservoir water level (m).
According to Equations (3) and (4), the water surface areas of the two reservoirs under any water level conditions can be calculated, so the water surface areas of the reservoirs under different water levels are calculated in sequence. The capacity between any two reservoir water levels can be calculated using the following formula:
formula
(5)
where Vi is the capacity between the water levels of the two reservoirs, hi is the difference between the water levels of the two reservoirs, and Ai and Ai−1 are the water surface areas corresponding to the water levels of the two reservoirs.
The calculation formula of cumulative capacity is as follows:
formula
(6)

Tables 5 and 6 show the water surface area and capacity of the two reservoirs at different water levels. Figures 6 and 7 are the characteristic curves of the two reservoirs.

Table 5

Relativity of water level–surface water area–reservoir capacity of Jinshuitan Reservoir

Water level (m)Surface water area (km2)Reservoir capacity (×108m3)
162.00 18.3661 4.5226 
163.00 18.8814 4.7088 
164.00 19.4130 4.9003 
165.00 19.9615 5.0971 
166.00 20.5275 5.2995 
167.00 21.1115 5.5077 
168.00 21.7142 5.7218 
169.00 22.3362 5.9421 
170.00 22.9779 6.1686 
171.00 23.6401 6.4017 
172.00 24.3233 6.6415 
173.00 25.0280 6.8882 
174.00 25.7549 7.1421 
175.00 26.5046 7.4034 
176.00 27.2776 7.6723 
177.00 28.0745 7.9490 
178.00 28.8960 8.2339 
179.00 29.7426 8.5271 
180.00 30.6148 8.8288 
181.00 31.5133 9.1394 
182.00 32.4387 9.4592 
183.00 33.3915 9.7883 
184.00 34.3724 10.1271 
185.00 35.3819 10.4758 
186.00 36.4205 10.8348 
187.00 37.4890 11.2044 
188.00 38.5879 11.5847 
189.00 39.7177 11.9762 
190.00 40.8790 12.3792 
191.00 42.0725 12.7939 
192.00 43.2988 13.2207 
192.70 44.1769 13.5269 
Water level (m)Surface water area (km2)Reservoir capacity (×108m3)
162.00 18.3661 4.5226 
163.00 18.8814 4.7088 
164.00 19.4130 4.9003 
165.00 19.9615 5.0971 
166.00 20.5275 5.2995 
167.00 21.1115 5.5077 
168.00 21.7142 5.7218 
169.00 22.3362 5.9421 
170.00 22.9779 6.1686 
171.00 23.6401 6.4017 
172.00 24.3233 6.6415 
173.00 25.0280 6.8882 
174.00 25.7549 7.1421 
175.00 26.5046 7.4034 
176.00 27.2776 7.6723 
177.00 28.0745 7.9490 
178.00 28.8960 8.2339 
179.00 29.7426 8.5271 
180.00 30.6148 8.8288 
181.00 31.5133 9.1394 
182.00 32.4387 9.4592 
183.00 33.3915 9.7883 
184.00 34.3724 10.1271 
185.00 35.3819 10.4758 
186.00 36.4205 10.8348 
187.00 37.4890 11.2044 
188.00 38.5879 11.5847 
189.00 39.7177 11.9762 
190.00 40.8790 12.3792 
191.00 42.0725 12.7939 
192.00 43.2988 13.2207 
192.70 44.1769 13.5269 
Table 6

Relativity of water level–surface water area–reservoir capacity of Shitang Reservoir

Water level (m)Surface water area (km2)Reservoir capacity (×108m3)
97.50 4.4011 0.4436 
98.00 4.6453 0.4662 
99.00 5.1379 0.5151 
100.00 5.6431 0.5690 
101.00 6.1662 0.6281 
102.00 6.7094 0.6924 
103.00 7.2713 0.7623 
104.00 7.8468 0.8379 
Water level (m)Surface water area (km2)Reservoir capacity (×108m3)
97.50 4.4011 0.4436 
98.00 4.6453 0.4662 
99.00 5.1379 0.5151 
100.00 5.6431 0.5690 
101.00 6.1662 0.6281 
102.00 6.7094 0.6924 
103.00 7.2713 0.7623 
104.00 7.8468 0.8379 
Figure 6

Characteristic curves of Jinshuitan Reservoir.

Figure 6

Characteristic curves of Jinshuitan Reservoir.

Figure 7

Characteristic curves of Shitang Reservoir.

Figure 7

Characteristic curves of Shitang Reservoir.

RESULTS

Accuracy analysis

The accuracy of the method of determining reservoir characteristic curves proposed in this paper mainly depends on three factors: (1) the number of images used; (2) the analysis and calculation of water surface area; and (3) the configuration of curves.

The quantity of remote sensing images used

For the same water level elevation difference, the more the satellite images collected, that is, the smaller the water level difference between two adjacent images, the higher the calculation accuracy. The total water level elevation differences of Jinshuitan Reservoir and Shitang Reservoir in this research were 20.5 m and 4.5 m and 25 and 11 satellite images were used, respectively. The average water level differences between two adjacent images were 0.79 m and 0.41 m, which could meet the requirements for determining the reservoir characteristic curve. However, weather and other factors created difficulties for the collection of satellite data of some water level segments, resulting in the water level difference between some images of Jinshuitan Reservoir exceeding 1 m and that between some images of Shitang Reservoir exceeding 0.5 m. For example, the water level difference between No. 4 and No. 5 in Table 1 reached 1.11 m, and that between No. 8 and No. 9 in Table 2 reached 0.67 m. After new information is obtained, supplementary information and revision can be made.

Analysis and calculation of water surface area

In theory, after the Earth resources satellite image data are geographically fine-corrected as per the 1:50,000 topographic map, it is equivalent to the topographic map whether in latitude and longitude, geographic coordinates, or kilometer grid, which can completely guarantee that its span error is kept within one pixel, that is, 15 × 15 m. Therefore, when the water area is counted on such images, the final global error is not more than 15 × 15 m. The water surface area is actually an irregular figure. It seems the total area of the water surface is obtained by placing the water surface area on a 15 × 15 m grid. Of course, for the actual water boundary, some will occupy more than half of the grid, some only occupy less than half of the grid. When the water area is counted using the images, those occupying more than half of the grid are included and those occupying less than half of the grid are rejected. Pursuant to the grid adjustment theory, the final error is a grid, namely, 15 × 15 m.

Curve configuration

In this paper, the polynomial curve equation configuration software under the platform of satellite remote sensing image processing system was used for curve configuration, which has avoided the error caused by previous methods. The statistical parameters of curve configuration are shown in Table 7.

Table 7

Parameters of polynomial fitting curve

Data numberCorrelation coefficientStandard deviationConfidence
Jinshuitan Reservoir 25 0.99976 0.07480 <0.0001 
Shitang Reservoir 11 0.99690 0.05393 <0.0001 
Data numberCorrelation coefficientStandard deviationConfidence
Jinshuitan Reservoir 25 0.99976 0.07480 <0.0001 
Shitang Reservoir 11 0.99690 0.05393 <0.0001 

In order to test the configuration accuracy of the polynomial curve equation, the water surface area corresponding to the reservoir water level on the 36 satellite images was calculated through the polynomial and compared with the original area, as shown in Tables 8 and 9. It can be seen from the tables that the absolute error of most points of Jinshuitan Reservoir is less than 0.1 km2, with an average of 0.012 km2, and the relative error is less than 0.5%, with an average of 0.05%, while the absolute error of most points of Shitang Reservoir is less than 0.01 km2, with an average of 0.009 km2, and the relative error is not more than 0.5%, with an average of 0.154%. Therefore, both the formulation parameters of the equation and the accuracy analysis of the curves show that the reservoir characteristic curve equation determined in this paper is highly accurate.

Table 8

Error of characteristic curves of Jinshuitan Reservoir

No.Water level (m)Calculated area (km2)Curve area (km2)Absolute error (km2)Relative error (%)
184.44 34.8737 34.8130 0.0607 0.17 
184.31 34.7038 34.6822 0.0215 0.06 
183.61 33.9818 33.9865 − 0.0047 − 0.01 
182.92 33.2294 33.3143 − 0.0849 − 0.26 
181.81 32.2776 32.2608 0.0168 0.05 
181.12 31.5551 31.6229 − 0.0678 − 0.21 
180.31 31.0212 30.8905 0.1307 0.42 
179.60 30.2879 30.2628 0.0251 0.08 
178.48 29.2928 29.2992 − 0.0064 − 0.02 
10 177.80 28.5496 28.7297 − 0.1801 − 0.63 
11 176.93 27.9569 28.0180 − 0.0610 − 0.22 
12 176.23 27.4939 27.4588 0.0351 0.13 
13 175.35 26.9453 26.7725 0.1729 0.64 
14 174.58 26.1826 26.1869 − 0.0043 − 0.02 
15 173.54 25.4898 25.4177 0.0721 0.28 
16 172.55 24.7678 24.7082 0.0596 0.24 
17 171.57 23.9141 24.0269 − 0.1128 − 0.47 
18 170.51 23.3894 23.3130 0.0764 0.33 
19 169.90 22.9507 22.9128 0.0378 0.16 
20 169.10 22.3882 22.3994 − 0.0113 − 0.05 
21 168.00 21.8333 21.7142 0.1191 0.55 
22 166.96 21.1086 21.0878 0.0208 0.10 
23 166.13 20.5781 20.6024 − 0.0243 − 0.12 
24 165.21 20.0918 20.0789 0.0129 0.06 
25 164.78 19.8374 19.8394 − 0.0020 − 0.01 
No.Water level (m)Calculated area (km2)Curve area (km2)Absolute error (km2)Relative error (%)
184.44 34.8737 34.8130 0.0607 0.17 
184.31 34.7038 34.6822 0.0215 0.06 
183.61 33.9818 33.9865 − 0.0047 − 0.01 
182.92 33.2294 33.3143 − 0.0849 − 0.26 
181.81 32.2776 32.2608 0.0168 0.05 
181.12 31.5551 31.6229 − 0.0678 − 0.21 
180.31 31.0212 30.8905 0.1307 0.42 
179.60 30.2879 30.2628 0.0251 0.08 
178.48 29.2928 29.2992 − 0.0064 − 0.02 
10 177.80 28.5496 28.7297 − 0.1801 − 0.63 
11 176.93 27.9569 28.0180 − 0.0610 − 0.22 
12 176.23 27.4939 27.4588 0.0351 0.13 
13 175.35 26.9453 26.7725 0.1729 0.64 
14 174.58 26.1826 26.1869 − 0.0043 − 0.02 
15 173.54 25.4898 25.4177 0.0721 0.28 
16 172.55 24.7678 24.7082 0.0596 0.24 
17 171.57 23.9141 24.0269 − 0.1128 − 0.47 
18 170.51 23.3894 23.3130 0.0764 0.33 
19 169.90 22.9507 22.9128 0.0378 0.16 
20 169.10 22.3882 22.3994 − 0.0113 − 0.05 
21 168.00 21.8333 21.7142 0.1191 0.55 
22 166.96 21.1086 21.0878 0.0208 0.10 
23 166.13 20.5781 20.6024 − 0.0243 − 0.12 
24 165.21 20.0918 20.0789 0.0129 0.06 
25 164.78 19.8374 19.8394 − 0.0020 − 0.01 
Table 9

Error of characteristic curves of Shitang Reservoir

No.Water level (m)Calculated area (km2)Curve area (km2)Absolute error (km2)Relative error (%)
102.23 6.8645 6.8371 0.0274 0.40 
102.12 6.7712 6.7759 − 0.0048 − 0.07 
101.84 6.6476 6.6212 0.0264 0.40 
101.56 6.4688 6.4680 0.0008 0.01 
101.10 6.2343 6.2197 0.0146 0.23 
100.89 6.0773 6.1077 − 0.0305 − 0.50 
100.23 5.7596 5.7617 − 0.0021 − 0.04 
99.90 5.6135 5.5918 0.0217 0.39 
99.23 5.2895 5.2527 0.0368 0.70 
10 98.67 4.9739 4.9743 − 0.0005 − 0.01 
11 97.96 4.6343 4.6257 0.0086 0.19 
No.Water level (m)Calculated area (km2)Curve area (km2)Absolute error (km2)Relative error (%)
102.23 6.8645 6.8371 0.0274 0.40 
102.12 6.7712 6.7759 − 0.0048 − 0.07 
101.84 6.6476 6.6212 0.0264 0.40 
101.56 6.4688 6.4680 0.0008 0.01 
101.10 6.2343 6.2197 0.0146 0.23 
100.89 6.0773 6.1077 − 0.0305 − 0.50 
100.23 5.7596 5.7617 − 0.0021 − 0.04 
99.90 5.6135 5.5918 0.0217 0.39 
99.23 5.2895 5.2527 0.0368 0.70 
10 98.67 4.9739 4.9743 − 0.0005 − 0.01 
11 97.96 4.6343 4.6257 0.0086 0.19 

Comparative analysis with existing characteristic curves

The existing characteristic curves of Jinshuitan Reservoir and Shitang Reservoir were obtained on April 20, 2007 via collation based on the data available at the time of database creation. The analysis mainly compared the water level–capacity curves in the research with the existing water level–capacity curves of the two reservoirs, as shown in Tables 10 and 11 and Figure 8.

Table 10

Comparative analysis between characteristic curves of Jinshuitan Reservoir

Water level (m)Capacity of existing characteristic curves (×108m3)Capacity of new characteristic curves (×108m3)Absolute error (×108m3)Relative error (%)
164.00 4.9003 4.9003 0.0000 0.00 
165.00 5.1107 5.0971 − 0.0135 − 0.26 
166.00 5.3272 5.2995 − 0.0277 − 0.52 
167.00 5.5501 5.5077 − 0.0424 − 0.76 
168.00 5.7793 5.7218 − 0.0575 − 0.99 
169.00 6.0153 5.9421 − 0.0733 − 1.22 
170.00 6.2576 6.1686 − 0.0889 − 1.42 
171.00 6.5058 6.4017 − 0.1041 − 1.60 
172.00 6.7605 6.6415 − 0.1190 − 1.76 
173.00 7.0221 6.8882 − 0.1338 − 1.91 
174.00 7.2900 7.1421 − 0.1479 − 2.03 
175.00 7.5644 7.4034 − 0.1610 − 2.13 
176.00 7.8457 7.6723 − 0.1734 − 2.21 
177.00 8.1339 7.9490 − 0.1848 − 2.27 
178.00 8.4290 8.2339 − 0.1951 − 2.31 
179.00 8.7307 8.5271 − 0.2037 − 2.33 
180.00 9.0393 8.8288 − 0.2104 − 2.33 
181.00 9.3552 9.1394 − 0.2158 − 2.31 
182.00 9.6786 9.4592 − 0.2194 − 2.27 
183.00 10.0094 9.7883 − 0.2211 − 2.21 
184.00 10.3483 10.1271 − 0.2212 − 2.14 
185.00 10.6958 10.4758 − 0.2199 − 2.06 
Water level (m)Capacity of existing characteristic curves (×108m3)Capacity of new characteristic curves (×108m3)Absolute error (×108m3)Relative error (%)
164.00 4.9003 4.9003 0.0000 0.00 
165.00 5.1107 5.0971 − 0.0135 − 0.26 
166.00 5.3272 5.2995 − 0.0277 − 0.52 
167.00 5.5501 5.5077 − 0.0424 − 0.76 
168.00 5.7793 5.7218 − 0.0575 − 0.99 
169.00 6.0153 5.9421 − 0.0733 − 1.22 
170.00 6.2576 6.1686 − 0.0889 − 1.42 
171.00 6.5058 6.4017 − 0.1041 − 1.60 
172.00 6.7605 6.6415 − 0.1190 − 1.76 
173.00 7.0221 6.8882 − 0.1338 − 1.91 
174.00 7.2900 7.1421 − 0.1479 − 2.03 
175.00 7.5644 7.4034 − 0.1610 − 2.13 
176.00 7.8457 7.6723 − 0.1734 − 2.21 
177.00 8.1339 7.9490 − 0.1848 − 2.27 
178.00 8.4290 8.2339 − 0.1951 − 2.31 
179.00 8.7307 8.5271 − 0.2037 − 2.33 
180.00 9.0393 8.8288 − 0.2104 − 2.33 
181.00 9.3552 9.1394 − 0.2158 − 2.31 
182.00 9.6786 9.4592 − 0.2194 − 2.27 
183.00 10.0094 9.7883 − 0.2211 − 2.21 
184.00 10.3483 10.1271 − 0.2212 − 2.14 
185.00 10.6958 10.4758 − 0.2199 − 2.06 
Table 11

Comparative analysis between characteristic curves of Shitang Reservoir

Water level (m)Capacity of existing characteristic curves (×108m3)Capacity of new characteristic curves (×108m3)Absolute error (×108m3)Relative error (%)
99.00 0.5151 0.5151 0.0000 0.00 
100.00 0.5762 0.5690 − 0.0072 − 1.25 
101.00 0.6412 0.6281 − 0.0131 − 2.04 
102.00 0.7073 0.6924 − 0.0149 − 2.11 
103.00 0.7737 0.7623 − 0.0114 − 1.47 
Water level (m)Capacity of existing characteristic curves (×108m3)Capacity of new characteristic curves (×108m3)Absolute error (×108m3)Relative error (%)
99.00 0.5151 0.5151 0.0000 0.00 
100.00 0.5762 0.5690 − 0.0072 − 1.25 
101.00 0.6412 0.6281 − 0.0131 − 2.04 
102.00 0.7073 0.6924 − 0.0149 − 2.11 
103.00 0.7737 0.7623 − 0.0114 − 1.47 
Figure 8

Comparisons between the characteristic curves of two reservoirs.

Figure 8

Comparisons between the characteristic curves of two reservoirs.

As indicated by the figure, at the same water level, the capacity value of the new characteristic curve is smaller than that of the existing characteristic curve. At the water level of 185 m, the capacity of Jinshuitan Reservoir is reduced by 2.06%, being 1.04758 billion m3, while the capacity of Shitang Reservoir at the water level of 103 m is reduced by 1.47%, being 76.23 million m3. After analysis, the reasons are as follows:

  1. After years of operation of the reservoir, soil erosion occurs in the reservoir area. This part of the soil is deposited in front of the dam, resulting in a decrease in reservoir capacity.

  2. Through the comparative analysis of satellite images over the years, urbanization has a great impact on the two reservoirs. After field study, part of the spoil from highway construction enters the reservoirs, and the reclamation and development is serious. These are the direct causes of the reduction of reservoir capacity.

Figures 9 and 10 are satellite images of Anren Town and Dayuan Township near Jinshuitan Reservoir and Shitang Reservoir before and after urbanization.

Figure 9

Landsat images before (a) and after (b) urbanization of Anren Town.

Figure 9

Landsat images before (a) and after (b) urbanization of Anren Town.

Figure 10

Landsat images before (a) and after (b) urbanization of Dayuan Town.

Figure 10

Landsat images before (a) and after (b) urbanization of Dayuan Town.

DISCUSSION

The method has effectively overcome the shortcomings of previous methods, and its advantages are as follows: (1) A large visual field. With a relatively big ground covering extent, the satellite data can fully and accurately describe the whole picture of the reservoir area, which is impossible with previous methods. (2) A fast cycle. The satellite data in the same region can be re-acquired every few days, so the data accumulation in each region is rich, and a series of data can be provided for reservoir dynamic changes under different conditions (e.g., different water levels). (3) Updated data. After the satellite passes the region, the ground station will receive the data. After initial processing, a map will be formed within a short period of time. It can comprehensively and quickly reflect the impact of the profile of the current basin, reservoir, and human activities on the reservoir area. (4) Extensive information. During satellite imaging, not only the visible bands but also the infrared band information that is invisible to the human eye are recorded, and converted to the image visible to human eyes, thus the recognition scope is expanded. If the water body is fully absorbed on the near-infrared band, the image will be black, while the land, vegetation, and other ground objects are strong diffuse reflection objects and they reflect the near-infrared band to different extents, which is enough to form a sharp contrast with the black water surface. This provides favorable conditions for the identification of water area. (5) Fewer constraints. The satellite is not limited by geographical and regional conditions. Even for remote and uninhabited areas, ideal data are also available. In addition, it is also not subject to climate (e.g., bad weather) and ground objects (e.g., vegetation height and density) and a great deal of hard field work is completed indoors. (6) High accuracy. Since the satellite remote sensing image is a high-resolution image, it is quite accurate to calculate water area based on the water range on the satellite image. In the meanwhile, various human errors and instrumental errors caused by the use of a planimeter for survey and calculation on the topographic map are also eliminated. In view of the above advantages, the remote sensing method of determining reservoir characteristic curves using the data of China–Brazil Earth Resources Satellite and Landsat was adopted.

CONCLUSIONS

The method of determining reservoir characteristic curve was studied using the CCD image data of China–Brazil Earth Resources Satellite and TM/ETM image data of Landsat with a high ground resolution through image processing technology such as computer classification combined with the accurate water level data of the reservoir area. The precision analysis and the comparison with the existing characteristic curves show that the reservoir characteristic curve determined using the method is highly accurate.

The application of satellite remote sensing technology to determine the reservoir characteristic curve has unique advantages. It can not only quickly and reliably obtain the ground information of an area usually lacking in data or an inaccessible area, but also save a large amount of expense compared with ground survey. Moreover, it can fully meet the needs of reservoir operation, management, and application.

With the enhancement of ground resolution of the Earth resources satellite, the further development of computer classification technology, and the support of GIS, the application of resource satellite data to water conservancy research has wide-ranging prospects.

ACKNOWLEDGEMENTS

This work was funded by the National Key Research and Development Program (Grant Nos. 2018YFC0407704 and 2016YFC0401005), and the National Natural Science Foundation of China (Grant Nos. 51779146 and 51879163).

DATA AVAILABILITY STATEMENT

Data cannot be made publicly available; readers should contact the corresponding author for details.

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